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Register a new userSubjective Quality Assessment of Ground-based Camera Images
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Image quality assessment is critical to control and maintain the perceived quality of visual content. Both subjective and objective evaluations can be utilised, however, subjective image quality assessment is currently considered the most reliable approach. Databases containing distorted images and mean opinion scores are needed in the field of atmospheric research with a view to improve the current state-of-the-art methodologies. In this paper, we focus on using ground-based sky camera images to understand the atmospheric events. We present a new image quality assessment dataset containing original and distorted nighttime images of sky/cloud from SWINSEG database. Subjective quality assessment was carried out in controlled conditions, as recommended by the ITU. Statistical analyses of the subjective scores showed the impact of noise type and distortion level on the perceived quality.
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In subjective full-reference image quality assessment, differences between perceptual image qualities of the reference image and its distort
Video live streaming is gaining prevalence among video streaming services, especially for the delivery of popular sporting events. Many objective Video Quality Assessment (VQA) models have been developed to predict the perceptual quality of videos. Appropriate databases that exemplify the distortions encountered in live streaming videos are important to designing and learning objective VQA models. Towards making progress in this direction, we built a video quality database specifically designed for live streaming VQA research. The new video database is called the Laboratory for Image and Video Engineering (LIVE) Live stream Database. The LIVE Livestream Database includes 315 videos of 45 contents impaired by 6 types of distortions. We also performed a subjective quality study using the new database, whereby more than 12,000 human opinions were gathered from 40 subjects. We demonstrate the usefulness of the new resource by performing a holistic evaluation of the performance of current state-of-the-art (SOTA) VQA models. The LIVE Livestream database is being made publicly available for these purposes at https://live.ece.utexas.edu/research/LIVE_APV_Study/apv_index.html.
To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably. The neglect of the high-level semantic information may result in predicting a clear blue sky as bad quality, which is inconsistent with human perception. Therefore, in this paper, we tackle this problem by exploiting the high-level semantics and propose a novel no-reference image quality assessment method for realistic blur images. Firstly, the whole image is divided into multiple overlapping patches. Secondly, each patch is represented by the high-level feature extracted from the pre-trained deep convolutional neural network model. Thirdly, three different kinds of statistical structures are adopted to aggregate the information from different patches, which mainly contain some common statistics (i.e., the mean&standard deviation, quantiles and moments). Finally, the aggregated features are fed into a linear regression model to predict the image quality. Experiments show that, compared with low-level features, high-level features indeed play a more critical role in resolving the aforementioned challenging problem for quality estimation. Besides, the proposed method significantly outperforms the state-of-the-art methods on two realistic blur image databases and achieves comparable performance on two synthetic blur image databases.
As the immersive multimedia techniques like Free-viewpoint TV (FTV) develop at an astonishing rate, users demand for high-quality immersive contents increases dramatically. Unlike traditional uniform artifacts, the distortions within immersive contents could be non-uniform structure-related and thus are challenging for commonly used quality metrics. Recent studies have demonstrated that the representation of visual features can be extracted from multiple levels of the hierarchy. Inspired by the hierarchical representation mechanism in the human visual system (HVS), in this paper, we explore to adopt structural representations to quantitatively measure the impact of such structure-related distortion on perceived quality in FTV scenario. More specifically, a bio-inspired full reference image quality metric is proposed based on 1) low-level contour descriptor; 2) mid-level contour category descriptor; and 3) task-oriented non-natural structure descriptor. The experimental results show that the proposed model outperforms significantly the state-of-the-art metrics.
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of image processing technology, perceptual image processing algorithms based on Generative Adversarial Networks (GAN) have produced images with more realistic textures. These output images have completely different characteristics from traditional distortions, thus pose a new challenge for IQA methods to evaluate their visual quality. In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual image processing algorithms and the corresponding subjective scores. Thus they can be used to develop and evaluate IQA methods on GAN-based distortions. The challenge has 270 registered participants in total. In the final testing stage, 13 participating teams submitted their models and fact sheets. Almost all of them have achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.
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